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gMapping Pieter Abbeel UC Berkeley EECS PowerPoint PPT Presentation


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gMapping Pieter Abbeel UC Berkeley EECS For more details, see paper: “Improved Techniques for Grid Mapping with Rao-Blackwellized Particle Filters” by Giorgio Grisetti , Cyrill Stachniss , Wolfram Burgard , IEEE Transactions in Robotics, 2006. TexPoint fonts used in EMF.

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gMapping Pieter Abbeel UC Berkeley EECS

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Gmapping pieter abbeel uc berkeley eecs

gMapping

Pieter Abbeel

UC Berkeley EECS

For more details, see paper: “Improved Techniques for Grid Mapping with Rao-Blackwellized Particle Filters” by Giorgio Grisetti, CyrillStachniss, Wolfram Burgard, IEEE Transactions in Robotics, 2006

TexPoint fonts used in EMF.

Read the TexPoint manual before you delete this box.: AAAAAAAAAAAAA


Gmapping overview

gMapping Overview

  • gMapping is probably most used SLAM algorithm

  • Implementation available on openslam.org (which has many more resources)

  • Currently the standard algorithm on the PR2


Problem formulation

Problem Formulation

  • Given

    • observations z1:t = z1, …, zt

    • odometry measurements u2:t = u1, …, ut

  • Find

    • Posterior p(x1:t, m | z1:t, u2:t )

    • With m a grid map


Key ideas

Key Ideas

  • Rao-Blackwellized Particle Filter

    • Each particle = sample of history of robot poses + posterior over maps given the sample pose history; approximate posterior over maps by distribution with all probability mass on the most likely map whenever posterior is needed

  • Proposal distribution ¼

    • Approximate the optimal sequential proposal distribution p*(xt) = p(xt | xi1:t-1, z1:t, u1:t) / p(zt | mit-1, xt ) p(xt | xit-1, ut) [note integral over all maps  most likely map only]

      • 1. find the local optimum argmaxx p*(x)

      • 2. sample xk around the local optimum, with weights wk = p*(xk)

      • 3. fit a Gaussian over the weighted samples

      • 4. this Gaussian is an approximation of the optimal sequential proposal p*

    • Sample from (approximately) optimal sequential proposal

  • Weight update for optimal sequential proposal is p(zt | xi1:t-1, z1:t-1, u1:t) = p(zt | mit-1, xit-1, ut-1), which is efficiently approximated from the same samples as above by

  • Resampling based on the effective sample size Seff


Motion model

Motion Model

  • Gaussian (EKF) approximation of odometry model from Probabilistic Robotics, pp. 121-123 [fix slide: for my edition of the book those pages describe the velocity motion model, not the odometry motion model]

  • Discrete time steps (=when updates happen) correspond to whenever the robot has traveled about 0.5m

  • From paper: “In general, there are more sophisticated techniques estimating the motion of the robot. However, we use that model to estimate a movement between two filter updates which is performed after the robot traveled around 0.5 m. In this case, this approximation works well and we did not observed a significant difference between the EKF-like model and the in general more accurate sample- based velocity motion model [41]”


Scan matching

Scan-Matching

  • Find argmaxx_t p(zt | mit-1, xt) p(xt | xit-1, ut)

  • p(xt | xit-1, ut) : Gaussian approximation of motion model, see previous slide

  • p(zt | mit-1, xt ) : “any scan-matching technique […] can be used”

    • Used by gMapping: “beam endpoint model”

      More on scan-matching in separate set of slides


Experiments

Experiments

  • “Most maps generated can be magnified up to a resolution of 1cm without observing considerable inconsistencies”

  • “Even in big real world datasets covering […] 250m by 250m, [..] never required more than 80 particles to build accurate maps.”

    Correctness evaluated through visual inspection by non-authors


Effect of proposal distribution

Effect of Proposal Distribution


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